LSI And Search Engine Optimisation
October 24th, 2008 by Nick
Search Engine Optimisation is one of the most jargonistic and acronym heavy online industries that a new Webmaster attempts get their head around. As such, it can prove very difficult for the first time or novice SEO (which also means Search Engine Optimiser as well as Search Engine Optimisation) to truly get to grips with the topic. Most recently, terms like LSI and SEPS have come to the fore because they offer a genuine insight into how the search engines value and subsequently rank organic websites crafted with the end user in mind.
Search Engine Indexing Techniques
Search engines use various techniques in order to try and promote high quality web pages and consign over optimised, or unnaturally written, websites to the lower rankings. The adoption of LSI, or Latent Semantic Indexing is one such move because it negates the need to over optimise content for a specific keyword and instead promotes the use of Related Keywords within the content of a page and within the building of a link profile.
Latent Semantic Indexing
Latent Semantic Indexing is not a search specific indexing technique and has been used by libraries and other major document indices in order to help cross referencing and searching. The principle is that a document that is indexed for one particular keyword can naturally be documented for a number of other topically or semantically related keywords. For example, a page on SEO could legitimately also be indexed under Internet marketing or online marketing because SEO is both of these.
Semantically Related Keywords
In order for search engines to legitimately use LSI within the formulation of their search results, they use semantic dictionaries. These are lists of keywords cross references to semantically related keywords. As well as using this list to identify other relevant keywords to index a page under, search engines also use this information to help them judge whether a site has been written organically or is designed solely to manipulate their search results.
Semantic Keyword Inclusion
Pages written about SEO should naturally contain other semantically related keywords. SEO is short for search engine optimisation and therefore the search engines would expect to see both of these terms on relevant pages. They would also look for other related keywords like those mentioned earlier – Internet marketing and Online Marketing. Pages that include these semantically related keywords are more likely to perform well in the search results.
Latent Semantic Indexing And Search Engine Optimisation
Search Engine Optimisation is constantly changing because search engines are always striving to improve their own search results. Their ultimate goal is to provide a list of high quality pages that are laser targeted to the term that a surfer used when searching their index. As a part of this, search engines naturally look for methods to promote sites that offer value and relevance to their own visitors. Latent Semantic Indexing is one such method and numerous patents have been filed by Google and other search engines related to this form of semantic indexing and topical indexing.
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Thanks for good content written, informative and useful article.
In search engine optimization keywords are very important and meaningful for search engine because it helps in the indexing and ranking of site.
In SEO field it is very new in the internet; it is very rich in acronym and is used because search engine keeps updating the site.
LSI for search engine confuses sometime because it gives ambiguous word for search engine and human as well.
The article should use fewer acronyms and they should be short because sometime it becomes less impressive and effective while human being reads the article of site.
I would like to clarify exactly how LSI works for you, it’s an important concept to understand, particularly for link building.
LSI does not require any sort of “semantically related” keyword list. LSI is based on an non-parametric statistical technique called principal component analysis, which decomposes functions into their axes of maximum variance. Basically, documents are converted into extremely sparse vectors of keyword counts (one dimension for each keyword). This is mildly problematic because doing any sort of calculation on a million+ dimension vector isn’t really feasible on a large scale. In order to solve this problem, LSI/PCA is used to transform the vector space into a low rank approximation (typically anywhere from 4-15 dimensions). Once the documents have been transformed into points in a low dimensional vector space, they can be quickly compared using the standard inner product for similarity.